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Snowflake COF-C02 Practice Test - Questions Answers, Page 39

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Question 381

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How should clustering be used to optimize the performance of queries that run on a very large table?

Manually re-cluster the table regularly.
Manually re-cluster the table regularly.
Choose one high cardinality column as the clustering key.
Choose one high cardinality column as the clustering key.
Use the column that is most-frequently used in query select clauses as the clustering key.
Use the column that is most-frequently used in query select clauses as the clustering key.
Assess the average table depth to identify how clustering is impacting the query.
Assess the average table depth to identify how clustering is impacting the query.
Suggested answer: B

Explanation:

For optimizing the performance of queries that run on a very large table, it is recommended to choose one high cardinality column as the clustering key.This helps to co-locate similar rows in the same micro-partitions, improving scan efficiency in queries by skipping data that does not match filtering predicates4.

References:[COF-C02] SnowPro Core Certification Exam Study Guide

asked 23/09/2024
Kinshuk Choubisa
44 questions

Question 382

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What step can reduce data spilling in Snowflake?

Using a larger virtual warehouse
Using a larger virtual warehouse
Increasing the virtual warehouse maximum timeout limit
Increasing the virtual warehouse maximum timeout limit
Increasing the amount of remote storage for the virtual warehouse
Increasing the amount of remote storage for the virtual warehouse
Using a common table expression (CTE) instead of a temporary table
Using a common table expression (CTE) instead of a temporary table
Suggested answer: A

Explanation:

To reduce data spilling in Snowflake, using a larger virtual warehouse is effective because it provides more memory and local disk space, which can accommodate larger data operations and minimize the need to spill data to disk or remote storage1. References: [COF-C02] SnowPro Core Certification Exam Study Guide

asked 23/09/2024
Himanshu Sachdeva
36 questions

Question 383

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Which VALIDATION_MODE value will return the errors across the files specified in a COPY command, including files that were partially loaded during an earlier load?

RETURN_-1_R0WS
RETURN_-1_R0WS
RETURN_n_ROWS
RETURN_n_ROWS
RETURN_ERRORS
RETURN_ERRORS
RETURN ALL ERRORS
RETURN ALL ERRORS
Suggested answer: C

Explanation:

TheRETURN_ERRORSvalue in theVALIDATION_MODEoption of the COPY command instructs Snowflake to validate the data files and return errors encountered across all specified files, including those that were partially loaded during an earlier load2. References: [COF-C02] SnowPro Core Certification Exam Study Guide

asked 23/09/2024
Hiren Patel
35 questions

Question 384

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Which solution improves the performance of point lookup queries that return a small number of rows from large tables using highly selective filters?

Automatic clustering
Automatic clustering
Materialized views
Materialized views
Query acceleration service
Query acceleration service
Search optimization service
Search optimization service
Suggested answer: D

Explanation:

The search optimization service improves the performance of point lookup queries on large tables by using selective filters to quickly return a small number of rows.It creates an optimized data structure that helps in pruning the micro-partitions that do not contain the queried values3. References: [COF-C02] SnowPro Core Certification Exam Study Guide

asked 23/09/2024
Bernardo Garcia
43 questions

Question 385

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Which Snowflake object does not consume any storage costs?

Secure view
Secure view
Materialized view
Materialized view
Temporary table
Temporary table
Transient table
Transient table
Suggested answer: C

Explanation:

Temporary tables do not consume any storage costs in Snowflake because they only exist for the duration of the session that created them and are automatically dropped when the session ends, thus incurring no long-term storage charges4. References: [COF-C02] SnowPro Core Certification Exam Study Guide

asked 23/09/2024
Franklin Leon
37 questions

Question 386

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Which function unloads data from a relational table to JSON?

TO_OBJECT
TO_OBJECT
TO_JSON
TO_JSON
TO_VARIANT
TO_VARIANT
OBJECT CONSTRUCT
OBJECT CONSTRUCT
Suggested answer: B

Explanation:

TheTO_JSONfunction is used to convert a VARIANT value into a string containing the JSON representation of the value. This function is suitable for unloading data from a relational table to JSON format. References: [COF-C02] SnowPro Core Certification Exam Study Guide

asked 23/09/2024
thanh nguyen
42 questions

Question 387

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Which commands can only be executed using SnowSQL? (Select TWO).

COPY INTO
COPY INTO
GET
GET
LIST
LIST
PUT
PUT
REMOVE
REMOVE
Suggested answer: C, D

Explanation:

TheLISTandPUTcommands are specific to SnowSQL and cannot be executed in the web interface or other SQL clients.LISTis used to display the contents of a stage, andPUTis used to upload files to a stage. References: [COF-C02] SnowPro Core Certification Exam Study Guide

asked 23/09/2024
ang kokwei
42 questions

Question 388

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What is the relationship between a Query Profile and a virtual warehouse?

A Query Profile can help users right-size virtual warehouses.
A Query Profile can help users right-size virtual warehouses.
A Query Profile defines the hardware specifications of the virtual warehouse.
A Query Profile defines the hardware specifications of the virtual warehouse.
A Query Profile can help determine the number of virtual warehouses available.
A Query Profile can help determine the number of virtual warehouses available.
A Query Profile automatically scales the virtual warehouse based on the query complexity.
A Query Profile automatically scales the virtual warehouse based on the query complexity.
Suggested answer: A

Explanation:

A Query Profile provides detailed execution information for a query, which can be used to analyze the performance and behavior of queries. This information can help users optimize and right-size their virtual warehouses for better efficiency. References: [COF-C02] SnowPro Core Certification Exam Study Guide

asked 23/09/2024
Franklin Adama
45 questions

Question 389

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What is the primary purpose of a directory table in Snowflake?

To store actual data from external stages
To store actual data from external stages
To automatically expire file URLs for security
To automatically expire file URLs for security
To manage user privileges and access control
To manage user privileges and access control
To store file-level metadata about data files in a stage
To store file-level metadata about data files in a stage
Suggested answer: D

Explanation:

A directory table in Snowflake is used to store file-level metadata about the data files in a stage. It is conceptually similar to an external table and provides information such as file size, last modified timestamp, and file URL. References: [COF-C02] SnowPro Core Certification Exam Study Guide

asked 23/09/2024
Mark Oh
36 questions

Question 390

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Which statements describe benefits of Snowflake's separation of compute and storage? (Select TWO).

The separation allows independent scaling of computing resources.
The separation allows independent scaling of computing resources.
The separation ensures consistent data encryption across all virtual data warehouses.
The separation ensures consistent data encryption across all virtual data warehouses.
The separation supports automatic conversion of semi-structured data into structured data for advanced data analysis.
The separation supports automatic conversion of semi-structured data into structured data for advanced data analysis.
Storage volume growth and compute usage growth can be tightly coupled.
Storage volume growth and compute usage growth can be tightly coupled.
Compute can be scaled up or down without the requirement to add more storage.
Compute can be scaled up or down without the requirement to add more storage.
Suggested answer: A, E

Explanation:

Snowflake's architecture allows for the independent scaling of compute resources, meaning you can increase or decrease the computational power as needed without affecting storage. This separation also means that storage can grow independently of compute usage, allowing for more flexible and cost-effective data management.

asked 23/09/2024
Cintron, Rigoberto
37 questions
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